Field of the Art
The disclosure relates to the field of image processing, and more particularly to the field of automated object detection using satellite imagery.
Discussion of the State of the Art
In recent years, there has been a substantial increase in the availability of high-resolution commercial satellite imagery, enabling a variety of new remote-sensing applications. One of the main challenges for these applications is the accurate and efficient extraction of semantic information from satellite imagery. An important instance of this class of challenges involves automatic detection of multiple objects in satellite images. Given the scale of the problem, one of the key challenges in learning object detectors is the acquisition and curation of labeled training data. Over the years, there has been a tremendous increase in both the amount and resolution of satellite imagery. This growth has resulted in several novel application opportunities, including everything from precision agriculture to automatic construction of three-dimensional terrain models. A key challenge shared among all these applications is the accurate and efficient extraction of semantic information from satellite imagery.
What is needed, is a means to accurately and efficiently identify and localize key objects in a region of significant geographic scale.